Processing facilities throughout the world largely depend on automatic control systems that primarily work off higher level mathematical formulas and complex algorithms. Most popular among them is the PID feedback controller. Process control environment is basically separated into two worlds; namely, the controller world which comprises the processing intelligence, and the process world where the actual mechanics of the process takes place.
A traditional PID process control is shown in FIG. 1. A setpoint SP is selected as the target at which to continuously run the process. A sensor 2 is attached to an output element 4 to provide process information, referred to as the feedback signal. This information is manipulated in the same unit as the SP by a math function unit 6. A PID 8 reacts to the difference, called the error signal, between SP and the process variable PV, by adjusting the output signal to compensate for the error. This circuit is referred to as the process loop. The error, output, and PV (and in some cases, SP) signals are a function of time f(t).
PID stands for the three main elements of a mathematical equation, namely, proportional, integral and derivative, from which the controller generates decisions and subsequent output signal. A theoretical equation of the PID is presented below to show the level of mathematics involved, and to demonstrate the level of skill and knowledge required to truly understand, appreciate, and use controllers based on PID.
The equation appears below: EQU P element I element D element O=A.DELTA.(t)+B(.intg..DELTA.(t)dt)+C(d.DELTA.(t)/dt),
where,
error signal.DELTA.=Setpoint-Process Variable.
Controller output is O. A, B, and C are constants which help to adjust or trim the influence, or effect of each element on the output signal. The value of these constants are directly related to the process physical characteristics the controller will be regulating.
PID controllers though widely used throughout the industry is seldom understood by all users mainly because of their extensive mathematically oriented mechanisms that govern its operation. Although mathematically oriented, it is difficult to configure the PID for a particular application based on calculations alone. Perhaps the most convenient way is through trial and error, and obviously, experience. As a result, not many users have the knowledge base to configure the PID for optimum process control even after many hours of use. Improvements, theories, and enhancements to make the PID more practical are still evolving through the years.
Present process control methods rely heavily upon higher level mathematics and algorithms, imaging the controller world orientation, coupled with advanced automation theory and control techniques. They can be extremely difficult to understand and therefore use. The synthesized model has to account for every conceivable situation for it to react properly to the process it attempts to control.
Regulating feed replenishing is a very common task found in every processing facility in the aggregate industry. Feed replenishing simply means to put-back material into the system, at a rate at which consumption equals output to sustain a desired throughput, or flow rate. It is applied to conveyors crushers, screens, bucket elevators, horizontal screws, etc.
Process control can be as simple as providing a high current alarm to inform the operator that the process had ventured beyond the limits of the electrical system's ability to provide energy for the additional burden or as formidable as including many sensors, level, temperature, pressure, etc., along with monitoring motor load into the process loop. Continuous process controllers provide the main means of feed regulation. These controllers are almost always PID, or PID based.
Owing to the complexity of precisely configuring the PID, it is not uncommon to find present users with the following typical frustrations:
A. PID control often results in large oscillatory swings on start-ups and during substantial disruptions in the PV, which could lead to equipment failure. It can have a tendency to overcorrect or undercorrect when it is extremely urgent to not do so.
B. PIDs require constant tuning. Because the PID is basically a mathematical model, unless the conditions which caused or permitted the equation to balance initially remains absolutely the same, the output cannot produce the same results when those conditions have changed, or are changing. The mechanics of processing systems in the aggregate industry are notoriously known to wear. Crusher liners begin to wear down the minute they are subjected to work. Bearing, rollers, and gears wear.
Another variable is the very feed material that the aggregate facility processes. Aggregate feed material seldom remains exactly the same from day to day, even hour to hour. The composition of the material changes. Particle size changes. And, for an aggregate crusher system, particle size change does matter. If the PID is set-up, tuned or configured with large size rock going through the crusher, it will certainly be most ineffective at maintaining the process when the feed material changes to smaller sized rock, because it requires more power to crush smaller rock than it does larger rock, assuming, however, that material has remained the same. Coupled with changing particle size, material density changes. If the PID is set-up, tuned or configured with softer rock, it will become ineffective in maintaining the process when the rock becomes harder. The obvious reason is that it takes more power to crush the same volume of hard rock than it would the softer rock. The PID based controller tuned to soft rock would be too sluggish dealing with hard rock. The combined effect of all these variables can be very frustrating for the users of PID based controllers and sometimes devastating on system mechanics.
With a variety of variables that a process controller is exposed to in aggregate processing, it is no wonder that the once tuned-to-peak-performance PID eventually becomes ineffective, de-tuned, and sluggish.
C. Precise PID configuration is complex. The output is the result of analytical calculations, dependent upon proportional, derivative, and calculus integral elements coupled with control algorithms. It is doubtful whether every PID user completely understands it and can therefore configure the PID for optimum process control performance. It is also doubtful whether every PID user understands the effect of each variable in the processing mechanics on the PID.
D. Furthermore, the magnitude of frustration magnifies exponentially when the task or application requires the use of multiple sensors into one PID controller. Anything short of optimum process control will inevitably result in inefficiency, production problems, and ultimately lost revenue as a function of time. And the magnitude is amplified when attempting to control a process at maximum loading.
There is, therefor, a need for an alternative to PID-based control in most present process control applications that utilize the PID to regulate physical quantities such as pressure, temperature, acceleration, revolutions per minute, flow rate and etc.
The present invention provides a simple method of process control, not involving high level mathematics, error signals, external setpoint, and PID process control methodology.